Triple
T17436333
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Red Beard |
E424009
|
entity |
| Predicate | stars |
P1956
|
FINISHED |
| Object | Toshirō Mifune |
—
|
NE NERFINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Toshirō Mifune | Statement: [Red Beard, stars, Toshirō Mifune]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Toshirō Mifune Context triple: [Red Beard, stars, Toshirō Mifune]
-
A.
Toshirō Mifune
chosen
Toshirō Mifune was a legendary Japanese actor best known for his iconic collaborations with director Akira Kurosawa in classic samurai and period films such as "Seven Samurai" and "Yojimbo."
-
B.
Tatsuya Nakadai
Tatsuya Nakadai is a renowned Japanese actor celebrated for his powerful performances in classic films by directors such as Akira Kurosawa and Masaki Kobayashi.
-
C.
Miko Nakadai
Miko Nakadai is a spirited Japanese exchange student and one of the main human allies of the Autobots in the animated series Transformers: Prime.
-
D.
Takeshi Katō
Takeshi Katō was a Japanese actor known for his supporting roles in numerous postwar films and television dramas.
-
E.
Seiji Noma
Seiji Noma was a prominent Japanese publisher and founder of the Kodansha publishing company, known for his major influence on modern Japanese literature and culture.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69d889d88b6081908bada047f5b3ba51 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e4490426008190b474ed76aca5d6f3 |
completed | April 19, 2026, 3:16 a.m. |
Created at: April 10, 2026, 5:46 a.m.